R Notebook

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library("hdxstats")
library("dplyr")
library("ggplot2")
library("RColorBrewer")
library("tidyr")
library("pheatmap")
library("scales")
library("viridis")
library("patchwork")
library("Biostrings")
library("xfun")

#data_filepath <- "vignettes/data/MBPqDF_test.rsd"
#hdx_data <- extract_hdx_data(data_filepath) # DONE


# TEST 1
# INPUT
data_selection <- hdx_data[,1:24]
all_peptides <- rownames(data_selection)[[1]]
starting_parameters <- list(a = NULL, b = 0.001,  d = NULL, p = 1)
# OUTPUT
results <- analyse_kinetics(data = data_selection, 
                            method = "fit", 
                            peptide_selection = all_peptides, 
                            start = starting_parameters)

[1] "INFO: Performing fitting of Deuterium uptake kinetics. Method: 'hdxstats::fitUptakeKinetics' "

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

[1] "model fit failed, likely exessive missing values"

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

[1] "model fit failed, likely exessive missing values"

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

[1] "model fit failed, likely exessive missing values"

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

[1] "Could not fit model, likely exessive missing values"

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

[1] "model fit failed, likely exessive missing values"

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

[1] "model fit failed, likely exessive missing values"

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

[1] "model fit failed, likely exessive missing values"

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

[1] "model fit failed, likely exessive missing values"

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

[1] "Could not fit model, likely exessive missing values"

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

[1] "model fit failed, likely exessive missing values"

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

[1] "model fit failed, likely exessive missing values"

results$method

[1] "fitUptakeKinetics"

graphics_kinetic <- visualise_hdx_data(results, type="kinetics") 

[1] "INFO: I found  104  models in your results data."

graphics_kinetic[[1]] | graphics_kinetic[[2]] | graphics_kinetic[[3]]

graphics <- visualise_hdx_data(results, type="forest") 

[1] "INFO: I found  104  models in your results data."

graphics[[1]] | graphics_kinetic[[1]]

graphics[[1]]$data

data_selection <- hdx_data[,1:100]
all_peptides <- rownames(data_selection)[[1]] # get all peptides
starting_parameters <- list(a = NULL, b = 0.0001,  d = NULL, p = 1)
# OUTPUT
results <- analyse_kinetics(data = data_selection, 
                            method = "dfit", 
                            peptide_selection = all_peptides[37], 
                            start = starting_parameters)

[1] "INFO: Performing differential fitting of Deuterium uptake kinetics. Method: 'hdxstats::differentialUptakeKinetics' "
[1] "INFO: You did not specify a 'formula' for your fitting model."
[1] "INFO: Fitting will be performed for (default): 'formula <- value ~ a * (1 - exp(-b*(timepoint)^p)) + d' "

graphics <- visualise_hdx_data(results, type="kinetics")

[1] "INFO: I found  7  models in your results data."

graphics

New Section

head(HOIP)

Error in h(simpleError(msg, call)) : 
  error in evaluating the argument 'x' in selecting a method for function 'head': object 'HOIP' not found

EXAMPLE: Tutorial 1

csv_filename <- "MBP.csv"
csv_filepath <- system.file("extdata", csv_filename, package = "hdxstats")
data <- read.csv(csv_filepath)

columns_names <- c("hx_time", "replicate_cnt", "hx_sample") # to merge into new column names
columns_fixed <- c("pep_sequence", "pep_charge")

# transform data
data_wide <- pivot_wider(data.frame(data),
                        values_from = d,  # Deu_Uptake
                        names_from = columns_names, # Exposure_Time, Replicate, Other
                        id_cols = columns_fixed) # Sequence, Charge

Error in pivot_wider(data.frame(data), values_from = d, names_from = columns_names,  : 
  could not find function "pivot_wider"

EXAMPLE: Tutorial 3 (HOIP)

HOIPpath <- system.file("extdata", "N64184_1a2_state.csv", package = "hdxstats")
HOIP <- read.csv(HOIPpath)

HOIP_wide <- pivot_wider(data.frame(HOIP),
                         values_from = Center, # Deu_Uptake
                         names_from = c("Exposure", "State"), # Exposure_Time, Replicate (NA), Conditions, Other
                         id_cols = c("Sequence")) # Sequence, Charge (NA)

EXAMPLE: Case study

secA <- read.csv("inst/extdata/Project_2_SecA_Cluster_Data.csv")

secA_wide <- pivot_wider(data.frame(secA),
                        values_from = "Center",  # Deu_Uptake
                        names_from = c("Exposure", "File", "State"), # Exposure_Time, Replicate, Conditions, Other (NA or "")
                        id_cols = c("Sequence", "z")) # Sequence, Charge

graphics <- visualise_hdx_data(results, type="kinetics") # READY
graphics <- visualise_hdx_data(results, type="forest") # READY 
graphics <- visualise_hdx_data(results, type="manhatten")# <<<<--- NEXT
graphics <- visualise_hdx_data(results, type="peptide")
graphics <- visualise_hdx_data(results, type="epitope", level="peptide")
graphics <- visualise_hdx_data(results, type="epitope", level="residue") # Return heatmap
graphics <- visualise_hdx_data(results, type="protection", level="peptide") # Return heatmap
graphics <- visualise_hdx_data(results, type="protection", level="residue") # Return heatmap
graphics <- visualise_hdx_data(results, type="protection", level="residue", pdb="my_pdb_path")


########################################################################
# TUTORIAL 1 & 2
#data_filepath <- "/homes/sanjuan/R/x86_64-pc-linux-gnu-library/4.1/hdxstats/extdata/MBP.csv"
#data_filepath <- system.file("extdata", csv_filename, package = "hdxstats")
#data_filepath <- "vignettes/data/MBPqDF.rsd" # or, CSV file

# TUTORIAL 3 & 4 (HOIP assays)
#data_filepath <- system.file("extdata", "N64184_1a2_state.csv", package = "hdxstats")

# TUTORIAL secA-casestudy
#data_path <- "inst/extdata/Project_2_SecA_Cluster_Data.csv"

# TUTORIAL flexible-fits.Rmd
#MBPpath <- system.file("extdata", "MBP.csv", package = "hdxstats")

#######################################################
data_filepath <- "vignettes/data/MBPqDF.rsd"
hdx_data <- extract_hdx_data(data_filepath) # DONE


# TEST 1
# INPUT
data_selection <- hdx_data[,1:24]
all_peptides <- rownames(data_selection)[[1]]
starting_parameters <- list(a = NULL, b = 0.001,  d = NULL, p = 1)
# OUTPUT
results <- analyse_kinetics(data = data_selection, 
                            method = "fit", 
                            peptide_selection = all_peptides, 
                            start = starting_parameters)
# TEST 2
# INPUT
data_selection <- hdx_data[,61:100]
all_peptides <- rownames(data_selection)[[1]]
starting_parameters <- list(a = NULL, b = 0.001,  d = NULL, p = 1)
# OUTPUT
results <- analyse_kinetics(data = data_selection, 
                            method = "fit", 
                            peptide_selection = all_peptides, 
                            start = starting_parameters)
# TEST 3
# INPUT
data_selection <- hdx_data[,1:100]
all_peptides <- rownames(data_selection)[[1]] # get all peptides
starting_parameters <- list(a = NULL, b = 0.0001,  d = NULL, p = 1)
# OUTPUT
results <- analyse_kinetics(data = data_selection, 
                            method = "dfit", 
                            peptide_selection = all_peptides[37], 
                            start = starting_parameters)

#######################################################
graphics <- visualise_hdx_data(results, type="kinetics") # This applies to both regular and differential fits
graphics <- visualise_hdx_data(results, type="forest") # This only applies to regular fits
graphics <- visualise_hdx_data(results, type="manhatten") # This only applies to differential fits
graphics <- visualise_hdx_data(results, type="epitope", level="peptide")
graphics <- visualise_hdx_data(results, type="epitope", level="residue") # Return heatmap
graphics <- visualise_hdx_data(results, type="protection", level="peptide") # Return heatmap
graphics <- visualise_hdx_data(results, type="protection", level="residue") # Return heatmap
graphics <- visualise_hdx_data(results, type="protection", level="residue", pdb="my_pdb_path")

# GUI: Make GUI by assembling these building blocks